| --- |
| license: cc-by-nc-4.0 |
| task_categories: |
| - tabular-classification |
| language: |
| - en |
| tags: |
| - synthetic |
| - test-data |
| - cybersecurity |
| - network-traffic |
| - intrusion-detection |
| - incident-response |
| - tabular |
| size_categories: |
| - 10M<n<100M |
| pretty_name: Free Synthetic Security Cascade (100M) |
| --- |
| |
| # Free Synthetic Security Cascade — 100M Rows |
|
|
| A free, fully synthetic dataset of 100,000,000 network flow records, generated for developers and builders working on intrusion detection, security analytics, and incident-response models — realistic network traffic without touching any real network, host, or personal data. |
|
|
| Every value in this dataset is artificially generated. No real captures, no scraped traffic, no real IPs or PII. What makes it different: most rows are ordinary background traffic, but malicious activity arrives in **cascades** — dense bursts of correlated events where one intrusion triggers a chain of follow-on activity, separated by genuine calm stretches. This mirrors how real incidents actually unfold, so a model trained here has to handle both the quiet and the storm, not just uniformly-sprinkled anomalies. |
|
|
| ## Schema |
|
|
| | Column | Type | Description | |
| |---|---|---| |
| | flow_id | string | Unique flow identifier | |
| | timestamp | timestamp[ms] | Flow start time | |
| | src_ip | string | Source IP address (synthetic) | |
| | dst_ip | string | Destination IP address (synthetic) | |
| | src_port | int32 | Source port | |
| | dst_port | int32 | Destination port | |
| | protocol | string | One of: TCP, UDP, ICMP | |
| | bytes_sent | int64 | Bytes sent in the flow | |
| | bytes_received | int64 | Bytes received in the flow | |
| | duration_ms | int64 | Flow duration in milliseconds | |
| | flag_pattern | string | TCP-style flag pattern (9 values, e.g. SYN-ACK-ACK, FIN-ACK, PSH-ACK) | |
| | cascade_id | string | ID linking flows in the same attack cascade (null for normal traffic) | |
| | cascade_type | string | Attack cascade type (null for normal traffic) | |
| | event_severity | string | One of: none, low, medium, high, critical | |
| | is_malicious_ground_truth | bool | Ground-truth label: true if the flow is part of an attack cascade | |
| |
| **Cascade types:** `port_scan_to_bruteforce`, `ddos_burst`, `exfiltration_pattern`, `credential_stuffing_wave`, `lateral_movement`. |
| |
| Normal background traffic has `cascade_id` and `cascade_type` null, `event_severity` of `none`, and `is_malicious_ground_truth` false. Malicious flows carry a shared `cascade_id` so you can group a full incident, a `cascade_type`, an elevated severity, and a true label. |
| |
| ## Format |
| |
| - Apache Parquet, Snappy compression |
| - One file, ~4.25 GB, 100,000,000 rows |
| - Loads cleanly with pandas, polars, DuckDB, PyArrow, or the `datasets` library |
| |
| ## Quick start |
| |
| ```python |
| import pandas as pd |
| df = pd.read_parquet("security_cascade_100M.parquet") |
| print(df.head()) |
| ``` |
| |
| Or with the `datasets` library: |
| |
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("ziadatalabs/FreeSyntheticSecurityCascade100M") |
| ``` |
| |
| Or with DuckDB (great for querying without loading it all into memory): |
| |
| ```sql |
| SELECT cascade_type, count(*) |
| FROM 'security_cascade_100M.parquet' |
| WHERE is_malicious_ground_truth |
| GROUP BY cascade_type; |
| ``` |
| |
| ## Notes |
| |
| - All data is synthetic and generated programmatically. Any resemblance to real hosts, IPs, or traffic is coincidental. |
| - IPs, ports, and flow stats follow realistic formats and distributions but describe no real network. |
| - The cascade clustering is **by design**: malicious events cluster into correlated bursts with quiet periods between them, so models trained on this data are stress-tested for real-world emergencies and incident response — ready for anything, not just evenly-spaced anomalies. |
| - `is_malicious_ground_truth` and `cascade_id` give you clean labels for both per-flow classification and full-incident grouping. |
| |
| ## License & Usage |
| |
| Released under **CC BY-NC 4.0** — free for personal, research, and educational use, with attribution, no commercial use. See the license for details. |
| |
| *Published by Zia Data Labs. We create synthetic data — and we give some of it away free, because good test data shouldn't be hard to find.* |
| |
